A multiple-input multiple-output (MIMO) communication system employs multiple (Nt) transmit antennas and multiple (Nr) receive antennas for data transmission. A MIMO channel formed by the Nt transmit and Nr receive antennas may be decomposed into NS independent channels, with NS≦min{Nt, Nr}. Each of the NS independent channels is also referred to as a spatial subchannel of the MIMO channel and corresponds to a dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity) over that of a single-input single-output (SISO) communication system if the additional dimensionalities created by the multiple transmit and receive antennas are utilized.
A wideband MIMO system typically experiences frequency selective fading, meaning different amounts of attenuation across the system bandwidth. This frequency selective fading causes inter-symbol interference (ISI), which is a phenomenon whereby each symbol in a received signal acts as distortion to subsequent symbols in the received signal. This distortion degrades performance by impacting the ability to correctly detect the received symbols. As such, ISI is a non-negligible noise component that may have a large impact on the overall signal-to-noise-and-interference ratio (SNR) for systems designed to operate at high SNR levels, such as MIMO systems. In such systems, channel equalization may be used at the receivers to combat ISI. However, the computational complexity required to perform equalization is typically significant or prohibitive for most applications.
Orthogonal frequency division multiplexing (OFDM) may be used to combat ISI without the use of computationally intensive equalization. An OFDM system effectively partitions the system bandwidth into a number of (NF) frequency subchannels, which may be referred to as sub-bands or frequency bins. Each frequency subchannel is associated with a respective subcarrier frequency upon which data may be modulated. The frequency subchannels of the OFDM system may experience frequency selective fading (i.e., different amounts of attenuation for different frequency subchannels) depending on the characteristics (e.g., multipath profile) of the propagation path between transmit and receive antennas. With OFDM, the ISI due to the frequency selective fading may be combated by repeating a portion of each OFDM symbol (i.e., appending a cyclic prefix to each OFDM symbol), as is known in the art. A MIMO system may thus advantageously employ OFDM to combat ISI.
In order to increase the transmission data rate and spectral efficiency of the system, spatial multiplexing can be utilized at the transmitter where different and independent data streams are sent over a plurality of spatial subchannels. The detection accuracy at the receiver can be severely degraded due to a strong multiple access interference (interference of different data streams transmitted from a plurality of antennas). Furthermore, spatial and frequency subchannels of the MIMO-OFDM system may experience different channel conditions (e.g., different fading and multipath effects) and may achieve different SNRs. Moreover, the channel conditions may vary over time.
In order to successfully mitigate the multiple access interference, effects of noise and fading, the minimum mean square error (MMSE) channel equalization is typically applied at the receiver. However, an estimated signal obtained from the MMSE algorithm contains a bias which represents a self noise that degrades detection accuracy. It is well known in the art that MMSE technique can be simplified if a direct inversion of the channel matrix is replaced by the QR decomposition of an augmented channel matrix (hereinafter abbreviated as QRMMSE detection). However, a direct bias removal in this particular case is computationally complex. Furthermore, calculation of noise variance that is required for outer channel decoding may also be difficult. Therefore, a simpler and more efficient technique for removing the bias is considered that provides better detection accuracy compare to the biased MMSE detection.
The maximum likelihood (ML) detection may also be applied at the receiver side to mitigate multiple access interference and to reduce the effects of channel noise. The ML algorithm is optimal in terms of detection accuracy because likelihoods for all symbol vectors that can be possibly transmitted from a plurality of antennas are evaluated. On the other side, computational complexity may be large especially in systems with high spectral efficiency that employ large number of transmit antennas and high-order modulation types.
The ML detection may also be implemented with a preprocessing based on QR decomposition (hereinafter abbreviated as QRML), and therefore it can share common algorithmic operations with the QRMMSE detection allowing a possibility for hybrid architecture that may perform both aforementioned detection algorithms.
Although the ML detection typically achieves better error rate performance than the MMSE detection, there are certain conditions where the MMSE solution may be more beneficial, not only because of the smaller computational complexity. The MMSE detection may be a better solution if user equipment is around the cell edge performing the handover when the information about modulation type of other cell is not known to the mobile user. Information about the modulation type is required to perform the maximum-likelihood candidate search, while it is not necessary for the MMSE detection. Furthermore, if the space-time diversity is utilized at the transmitter (such as Alamouti scheme), MMSE and ML detection algorithms provide very similar error rate performance, while the computational complexity and power consumption of the MMSE detection may be substantially lower than for the ML solution.